The present invention relates generally to the field of consumer credit reporting and more particularly to the field of credit payment data collection and verification methods.
Traditional consumer credit payment data collection methods rely on obtaining payment information from the creditor. Using the traditional approach, creditors report payments either as “on time” or “late” in 30 day increments. The problem with the traditional methodology is that many smaller creditors and “non-traditional” creditors such as apartment rental landlords, utility and telephone service providers do not report to traditional credit bureaus because of technical barriers and/or lack of convenience. This condition causes many borrowers that faithfully pay on time to not receive that recognition by automated credit underwriting technologies that rely on automated payment data from the credit bureaus.
Distinct social and racial disparities associated with other-wise effective automated underwriting credit risk management technology have been observed since the introduction of this technology, especially in connection with “traditional” credit data collection and reporting practices. For example, the current housing credit application process for both residential leases and mortgages often presents a daunting problem to low and moderate income consumers, first-time homebuyers, and consumers with rehabilitated credit. In order to qualify for housing credit, such consumers must establish credit-worthiness using “traditional” credit instruments and payment history collection and reporting practices. However, the “traditional” methods used to establish credit-worthiness present distinct unfair disadvantages, especially to fiscally responsible low- and moderate-income consumers who pay their residential rent or mortgage, utilities, phone, retail credit bills on time, but who do not have other lines of credit. FICO (Fair, Isaac & Co.) credit scores are automatically calculated by “traditional” credit bureaus for their subscribers using FICO's proprietary algorithms. These credit scores are in turn used by automated mortgage underwriting and lease application scoring models to establish credit-worthiness and ultimately determine the likelihood of default, and whether the applicant will qualify for the housing credit sought.
One of the problems associated with “traditional” credit reporting methods is that no effort has been made in the market to systematically (i) collect residential rental lease, utility, or telephone payment data, (ii) assure data quality, (iii) prevent the effective prejudice of consumer's rights in the data collection process, and (iv) reduce the cost of processing payments. And in many cases, no effort has been made to collect residential mortgage payment data from so-called “subprime” lenders while assuring privacy of the creditor/debtor relationship. The FICO scores generated for low- and moderate-income consumers who do not have “traditional” lines of credit, do not own their own homes, and/or whose mortgages are serviced by a non-reporting mortgage servicer are therefore based solely on “traditional” credit history (which is defined as credit for retail goods and services such as car loans or credit cards) and often do not include “housing” credit history (which is defined as residential lease or mortgage payments and other housing-related payments such as mobile home pad rent, condominium, and cooperative, payments).
“Traditional” consumer credit data collection, management, and reporting practices are a problem in connection with housing credit for three (3) reasons. First, the FICO scores for low and moderate income consumers who make their residential lease, utility, and telephone payments on time are lower than they should be. Second, FICO scores that are based solely on retail (non-housing) credit information are not as accurate in predicting the likelihood of default on a residential mortgage or lease as a credit score in which housing credit data (if electronically accessible) is assessed and more heavily weighted than the retail credit data. This is because the correlation between an applicant's past housing credit payment behavior and their future housing credit payment behavior is believed to be a stronger default indicator than the correlation between past “traditional” (retail credit only) payment behavior and future housing credit payment behavior.
Yet another problem with the current system is that there is no check of the accuracy of the payment information that is reported to “traditional” credit bureaus. This is problematic for two reasons. First, it subjects consumers to potentially unscrupulous and unilateral payment reporting actions of creditors. Second, and of greatest concern, a consumer who rightfully withholds a payment due to a legitimate dispute (such as a landlord's failure to provide heat in the winter, running water, or sanitary plumbing) can receive a “black mark” using “traditional” credit reporting methods, and have their credit damaged for many years because of the rightful withholding of payment. This potential threat effectively prejudices a consumer's legal rights in a residential lease transaction using “traditional” credit reporting methods.
The Preferred Credit Information (PCI) Data Collection Methodology is an equitable and cost-effective system and method that uniquely addresses the aforementioned problems, and mitigates the social and racial disparities associated with automated underwriting technology.